A Visible Instrument for Product Discovery

One technique to drive this product discovery work is to make use of a psychological mannequin known as an Alternative Answer Tree (OST). To assist product groups successfully uncover and seize worth, we’ll stroll by what a chance resolution tree is, easy methods to use one, an instance of an OST, and easy methods to join your OST to analytics and experiments.

What’s product discovery?

Product discovery is the set of processes that groups use to unveil and make clear the wants of their prospects. By understanding the sorts of ache factors that prospects expertise, we are able to prioritize the sorts of options that can create worth. Product discovery is an important subset of the general product growth course of, because it helps product groups make knowledgeable choices about which product concepts to pursue and which to put aside.

Product discovery consists of buyer interviews, buyer shadowing, surveys, buyer suggestions, and prototype testing. Product discovery ought to occur frequently and ceaselessly. In different phrases, discovery shouldn’t simply occur on a quarterly foundation; relatively, it ought to happen daily, inside each product pod, in small ongoing increments.

Buyer-focused product discovery allows us to maximise our understanding of what creates worth for patrons, and to replace our strategy in actual time as we repeatedly study.

However, whereas product discovery is essential to changing buyer wants into enterprise worth, this important work is usually criticized or deprioritized by executives and cross-functional stakeholders. Let’s dive into why this would possibly occur.

Why would possibly product discovery run into adoption challenges?

Whereas most leaders and stakeholders gained’t object in precept to product discovery, many product groups nonetheless frequently run into adoption challenges with their product discovery practices.

It’s because many leaders are uncomfortable with undirected discovery; that’s, too ceaselessly, product groups will embark on a “discovery safari” to “take within the sights,” which causes leaders to query whether or not this analysis is sufficiently focused to drive enterprise worth.

Particularly, leaders wish to have solutions to the next sorts of questions:

• How do we all know that discovery will transfer a enterprise metric that issues?
• How lengthy is that this discovery work going to take, and the way a lot does it value?
• How do we all know that this funding will repay?

In our teaching observe at Product Instructor, we discover that many product managers initially fail to deal with these questions upfront, inflicting pointless friction and pressure. We’ve discovered that by introducing the chance resolution tree to our purchasers’ working processes inside their organizations, product groups are considerably extra seemingly to achieve advocating for customer-driven discovery.

So, let’s dig into what alternative resolution bushes are, and the way they ship worth throughout all components of the group.

What is a chance resolution tree (OST)?

The chance resolution tree framework was initially designed by a Stanford College design professor. In 2016, Teresa Torres, founding father of Product Discuss, utilized this framework to product discovery processes.

Each alternative resolution tree accommodates 4 key elements:

1. Metric: the business-relevant metric that guides discovery
2. Alternative: the ache factors that prospects have
3. Answer concepts: the doable ways in which we as a product staff can handle buyer ache
4. Checks: the experiments we are able to run to validate or invalidate our options, enabling us to de-risk our options and swiftly ship iterative worth

Under is an instance of what a chance resolution tree would possibly appear to be. We’ve coloration coded metrics blue, alternatives inexperienced, resolution concepts yellow, and experiments orange. Don’t fear about studying the textual content for every. We’ll dive deeper into this instance in a later part.

Why did Torres create the chance resolution tree mannequin? She did so based mostly on her observations in coaching product groups by product discovery. She seen that groups wanted a visible construction for guiding their discovery efforts, aligning proposed options vs. found buyer pains, and securing buy-in throughout departments.

Torres drew on a way taught by Stanford professor Bernie Roth. Professor Roth requested how folks’s desired options linked to their underlying wants and ache factors, after which requested members to conduct “divergent solutioning” to provide you with totally different potential options for fixing the identical ache factors.

By framing this tree-like set of questions as a visible graph of a chance resolution tree, Torres discovered that product groups have been more likely to advocate for options that actually addressed the ache of their prospects.

How does a chance resolution tree enhance discovery adoption?

Crucially, the chance resolution tree visualization does the next for product managers, govt leaders, and cross-functional stakeholders:

• Establishes a business-critical metric as a focusing lens for product discovery
• Focuses efforts on fixing buyer ache relatively than constructing a guidelines of options
• Converts discovery insights into “alternative areas” to put money into
• Shifts the dialogue away from “function supply” in the direction of “fast experimentation”
• Ties experiments again to buyer ache and iteration alongside prospects, relatively than non-interactive inside brainstorming

Earlier, we talked about that leaders are eager on understanding the enterprise worth that product discovery would possibly convey, and the potential prices or dangers related to product discovery. By framing discovery into business-relevant outcomes, product groups can ease fears round whether or not product discovery insights will likely be actionable or not.

And, with a chance resolution tree in place, groups are a lot much less prone to over index on a given resolution. As a substitute, they’ll take the time to ask what alternative or buyer ache they’re fixing, and use this broader view to innovate and provide you with higher-impact, lower-cost options that in the end create extra ROI.

The visible construction of the chance resolution tree clearly reduces the precedence of any given function because the “finish consequence,” and as an alternative strengthens the message that transferring the business-relevant metric is what really issues.

Subsequently, product groups that efficiently introduce alternative resolution bushes to their orgs have a tendency to seek out that stakeholders are far more prepared to run with experimentation, relatively than specializing in a laundry listing of options with deadlines.

How do alternative resolution bushes work?

Let’s break down every of the 4 key elements of alternative resolution bushes, and talk about greatest practices for every.

Metrics

The metric to your alternative resolution tree needs to be aligned with a KPI (key efficiency indicator) out of your OKRs (targets and key outcomes) in order that your work is straight tied to enterprise success. This metric is the lens by which you’ll conduct your product discovery.

In different phrases, any buyer conversations or product discovery efforts that will not transfer the metric shouldn’t be thought of. Solely the initiatives and efforts which have an actual chance of fixing the metric needs to be actively investigated.

However how can we choose a superb metric? Good metrics ought to steadiness the stress between “enterprise success” and “proximity to product.”

If the metric is just too distant from the product (e.g., companywide income or companywide income), then the product staff will wrestle to drive any type of actual focus of their discovery efforts. Theoretically, any initiative might be justified as a technique to enhance income or minimize prices.

And, if the metric is just too near the product (e.g., function click on charges), then the staff is over listed on a selected resolution, and never centered sufficient on buyer ache or funding alternatives that can transfer the needle for the enterprise.

A superb metric ought to monitor that worth has been created for patrons, and that worth has been captured for the enterprise. Examples would possibly embody time on platform, or month-to-month energetic customers.

Alternatives

Simply because we all know what metric we’re going after, doesn’t imply that we all know what pains our prospects are experiencing. Many instances, waterfall-oriented groups provide you with concepts “contained in the 4 partitions of the corporate” with out studying from prospects about what their pains are.

Alternatives have to create new worth for patrons, whereas additionally enabling the corporate to seize that worth within the type of utilization, income, referrals, and different related enterprise drivers.

Product groups can leverage any of the beneath to determine alternatives:

• Inbound buyer suggestions
• Outbound 1:1 interviews
• Outbound surveys
• Product information analytics
• Discussions with inside stakeholders (e.g., help, advertising, gross sales)

To make this idea extra concrete, let’s take into account Gmail, which is an e-mail inbox supplied by Google.

A chance is not “I need a greater spam filter.” This can be a resolution thought, and we’ve fallen for the entice of listening for what to construct relatively than which pains to unravel.

After we take into account the function thought “I need a greater spam filter,” this would possibly truly level to quite a lot of totally different underlying consumer pains, resembling:

• It takes me too lengthy to discover a particular e-mail
• I don’t like receiving emails from folks I don’t know
• I’m annoyed that e-mail notifications hold interrupting me throughout displays

As you may think, the set of doable options to deal with the chance of “discovering an e-mail quicker” vs. the set of doable options for the chance of “forestall emails from interrupting me” are wildly totally different from each other. Whereas it’s true that they share the identical function thought of “I need a greater spam filter,” that doesn’t imply {that a} spam filter is one of the best ways to realize both goal.

That’s why it’s essential for us to conduct qualitative buyer interviews and quantitative analytics to grasp the true ache that’s being expressed behind any given function request.

Moreover, any time our colleagues suggest options, we should always not take the function at face worth. As a substitute, we should always ask “what underlying ache does this function request clear up for a buyer?” From there, we are able to then take into account the complete set of resolution concepts, lots of which may be extra satisfying, simpler to construct, and simpler to take care of than the preliminary proposed thought.

Answer concepts

When you’ve chosen a chance, accomplice with design and engineering to determine quite a lot of doable methods to assault the shopper drawback:

• Be divergent and generate as many concepts as you’ll be able to
• Don’t be afraid of “dangerous” concepts as a result of they’ll spark good concepts
• You’ll converge on “what to ship” later throughout experimentation

Design may help you take into account how folks at present handle this drawback in the actual world. They will assess how rivals clear up this drawback by their merchandise, and so they also can determine how prospects use substitutes, options, or handbook processes to alleviate their ache. This 360-degree view allows us to provide you with a lot better concepts for options.

Engineering may help you identify whether or not this buyer ache will be abstracted right into a broader ache that encompasses a number of associated use circumstances. In some cases, many pains might sound fully unrelated at a consumer stage, however may be simply solved in a single fell swoop at a system stage.

For instance, say that you simply’re accountable for a mission administration platform like Asana or Trello. Some customers wish to know which duties are liable to lacking their deadlines. Some customers wish to know whether or not some assignees are overburdened with work. Some customers wish to perceive which departments made essentially the most job requests. And a few customers wish to know which teams have completed essentially the most duties.

These all appear wildly separate from a consumer ache perspective, however engineering can determine that the underlying consumer ache is “we are able to’t simply group or filter issues collectively based mostly on attributes that we have already got about every job.” And, from that lens, engineering can suggest a extremely versatile sort-and-filter system that will not have been prompt by any single consumer.

Lastly, don’t be afraid to solicit suggestions from cross-functional companions! In any case, they could have function requests available so that you can take into account.

However, as we talked about earlier than: when partaking with cross-functional suggestions on resolution concepts, you’ll want to make sure that these truly match the chosen alternative space that you simply’ve prioritized. You don’t wish to merely deal with the answer concepts that can assist shut a deal or fulfill a very vocal buyer.You wish to ship essentially the most doable worth, utilizing the fewest assets crucial, within the quickest doable time.

Checks & experiments

To attain excellent ROI, resolution concepts shouldn’t be absolutely shipped as-is, as a result of that’s costly and high-risk. Inside every resolution, we have now a number of underlying assumptions–and, we should always check every of those assumptions by well-designed low-effort experiments. This strategy reduces threat, drives studying, and in the end will increase ROI.

For instance, say that you simply’re accountable for a contract software program platform like Ironclad, and also you’d wish to ship performance that mechanically plugs in related contract phrases based mostly on AI/ML. Quite than constructing all the suggestion system in a single swing, you’ll wish to de-risk this initiative by breaking it down into smaller items.

One assumption may be “we imagine that legal professionals wish to assessment the suggestion earlier than inserting it into the contract.” If that’s the case, then the UI that we ship ought to let legal professionals see the contract clause that’s being prompt, and provides them the power to just accept or reject.

But when our assumption is “legal professionals are comfy with software program filling this in for them, and having to take a look at dozens of apparent strategies is a waste of their restricted time”, then we shouldn’t construct this UI.

How would we check this? We might run a “individual within the machine” check with a lawyer to see whether or not they’re comfy with contract clauses being inserted with out them taking a look at it.

One other assumption we’d want to check is “contract clauses are constant throughout totally different sorts of contracts.” This may not be true. Completely different contracts could require clauses to be edited earlier than inserting. In that case, we’d additionally wish to run an experiment right here.

For every experiment, we have to ensure that we’ve completed the next:

• Determine all of our implicit and express assumptions about our prospects, our rivals, and the expertise that we’re utilizing
• Create upfront thresholds for “how a lot the metric ought to transfer” for us to say that it was profitable
• Draft predetermined subsequent steps based mostly on experiment efficiency, overlaying what you’ll do in case your assumptions are validated, in addition to what you’ll do in case your assumptions are disproved

How can we prioritize the totally different experiments that we wish to run in opposition to our resolution concepts? Usually, we wish to prioritize with these three elements in thoughts:

1. Which experiments will transfer the overarching OST metric essentially the most?
2. Which experiments will de-risk the largest assumptions?
3. Which experiments will value us the least effort and time?

When to make use of a chance resolution tree

The purpose of a chance resolution tree is to arrange the potential areas of alternative the place the corporate can make investments; in different phrases, it categorizes viable ache factors that we are able to clear up for our prospects.

Subsequently, alternative resolution bushes needs to be used as an enter into any type of roadmapping train, whether or not we’re constructing roadmaps at a quarterly stage or an annual stage. In any case, we are able to’t construct efficient roadmaps if we don’t know which ache factors our prospects have.

Alternative resolution bushes are additionally fairly invaluable for framing product proposals, particularly after we plan on presenting these proposals to govt groups. By making certain that we’re aligned with our executives on the important thing metrics that matter, and by offering an area to collaboratively uncover the totally different alternatives accessible to us, we are able to extra successfully iterate by doable options and ship extra worth with much less effort and fewer time.

Many product groups run common product technique offsites. The subsequent time you attend a technique offsite, take into account pitching “alternative resolution bushes” as an actionable subsequent step for all attendees after the offsite has concluded.

And, for future technique offsites, take into account asking organizers to start with a assessment of any present OSTs that groups have been actively engaged on. By doing so, all attendees may have a shared view of the chance house forward, in addition to the progress that’s been made on tackling these alternatives.

An actual-world instance of a chance resolution tree

To convey the idea to life, let’s take into account a real-world instance of alternative resolution bushes in observe. Let’s say that you simply’re liable for enhancing the search functionality inside your B2B product, which is an internet site builder (e.g., Squarespace, WordPress, Wix, and so on.).

You might need outlined your metric of success to be “the typical variety of searches accomplished, per week, per buyer.” That’s, in case your search functionality is offering worth to your prospects, you then would count on that your prospects’ finish customers would more and more use your search performance, relatively than the performance of another competitor resolution (e.g., Google Customized Search).

By means of consumer analysis and inbound buyer suggestions, we’d have found that there are three core alternatives for enchancment:

1. Search outcomes inside your function are at present fairly troublesome to sift by
2. Customers have to attend a very long time for search outcomes to come back again, which is irritating
3. Many customers are including “native” location-based key phrases (e.g. “New York” or “San Diego”) to their queries, which is repetitive and tedious but crucial for correct outcomes

We would provide you with a chance resolution tree just like the beneath:

You’ll have labored by discussions with gross sales, advertising, buyer success, buyer help, design, and engineering to determine doable resolution concepts for every of the three alternatives.

And, for any given thought, you don’t merely ship that concept from begin to end – you create testable hypotheses and experiments to determine which features of the concept are most precious.

For instance, maybe you wish to make it simpler for customers to sift by search outcomes, and one concept that appears compelling is to point out consequence previews to customers.

“Consequence previews” is kind of broad, nonetheless; what precisely makes a preview compelling?

Say that we wish to check the idea that “each consequence needs to be reviewed by customers.” In that case, we might spin up an experiment the place each returned consequence has its personal text-based preview related to it.

However, we might additionally check the flip facet of this assumption, the place “a consumer actually solely simply needs a single greatest consequence ASAP.” We’d additionally wish to run an experiment with a single highlighted “most related” textual content snippet on the prime of the web page.

And, how do we all know that textual content is de facto one of the best ways ahead? What if our customers are significantly all in favour of photos, just like Pinterest or Instagram? In that case, as an alternative of surfacing text-based hyperlinks, we should always run an experiment the place we floor image-based snapshots for our search outcomes as an alternative.

The interplay between OSTs and product technique

Alternative resolution bushes work together with product technique in two key methods.

First, the OST technique enhances and enhances product technique. In any case, each OST begins from the product technique, as the chosen metric for any OST should align with the recognized product technique.

Second, alternative resolution bushes additionally inform and affect product technique, based mostly on assumptions that we’ve validated or disproven, and based mostly on the newly-discovered insights that we’ve uncovered from our experiments.

As a rule of thumb, refine your product technique after each three experiments. Ask these questions frequently as you full every experiment:

• Is the recognized buyer phase nonetheless the correct one to pursue?
• Is the chance bigger or smaller than we initially believed?
• Has the market turn into simpler or tougher to strategy?

Aligning and collaborating with stakeholders by OST

Individuals make choices based mostly on the context that they’ve, and cheap folks make the identical choices, if they’ve the identical context.

For those who’re confronted with disagreement, one (or each) of those is occurring:

• The disagreeing individual doesn’t have your context
• You don’t have the context that the disagreeing individual does

We are able to use alternative resolution bushes to convey stakeholders and executives with us on the invention journey, ranging from buyer learnings and ending with our conclusion on what to ship subsequent.

Let’s break down easy methods to arrange alternative resolution bushes inside your group. This playbook relies on the staff workshops that we’ve run with Fortune 500 firms, in addition to the 1:1 govt teaching that we offer to high-potential product leaders.

Under, we’ll cowl:

1. How you can set up your first OST
2. How you can mature an present OST
3. How you can pivot in the direction of a brand new OST

Establishing your first OST

Any time we introduce new working processes or frameworks for pondering inside our organizations, we have to safe the buy-in of leaders and cross-functional counterparts.

To interrupt down this support-building step, we’ve discovered that essentially the most profitable product managers have a tendency to make use of no less than 4 conferences to align stakeholders in the direction of product discovery practices. Listed below are the 4 conferences, and the subjects to cowl inside every:

1. Metric alignment: Align on the important thing enterprise metric and talk about how discovery will result in higher outcomes than waterfall over the long term.
2. Alternative exploration: Stroll by an preliminary set of alternative areas based mostly on buyer suggestions, and invite stakeholders to share their alternatives as effectively.
3. Alternative choice: Collectively choose a chance to start out with (not which one is “proper” or “flawed”), and doc your decision-making course of collectively.
4. Answer ideation: Collectively ideate quite a lot of options and agree which preliminary resolution to discover by experiments.

You don’t wish to rush by these conferences, as a result of the journey is extra necessary than the result. For those who discover {that a} explicit stakeholder is silent, adversarial, or just confused, take a while to have interaction with them one-on-one exterior of those conferences. Study what their considerations are, and work in partnership with them to deal with these considerations.

And, you could want greater than 4 conferences to convey stakeholders with you on the journey. That mentioned, we don’t advocate cramming a number of subjects into the identical assembly, as that tends to trigger fatigue, loss-of-focus, and frustration from stakeholders.

As an apart, For those who’d wish to study extra about easy methods to navigate stakeholder preferences extra broadly, we’ve recorded a one-hour lecture as a part of our month-to-month recorded PM lessons.

Again to the subject at hand: we now have our preliminary alternative resolution tree in place. However, we’re not completed but.We nonetheless have to provide you with experiment concepts, and we have to share experiment progress and general metric actions with our stakeholders.

Maturing an present OST

As you create every experiment thought, replace your OST to determine which resolution thought you’re addressing with every experiment. The place you’ll be able to, annotate your experiment with a one-sentence rationalization in regards to the speculation you search to check.

Then, as your experiments conclude, floor the outcomes of every experiment in your OST as effectively. You should use a checkmark for validated hypotheses and an X for invalidated hypotheses, in order that stakeholders can rapidly see your progress with out moving into the main points of particular metric outcomes.

You should definitely broadcast updates to your stakeholders and managers each one to 2 weeks, relying on how rapidly your experiments resolve. By doing so, you show that your discovery efforts are bearing fruit, and that you simply’re not merely speaking to prospects for the sake of speaking to prospects. As a substitute, your discovery work is actively transferring enterprise metrics ahead.

In your first two or three broadcasts, you’ll wish to pair these with dwell conferences as effectively. In these dwell conferences, you’ll wish to recap that the objective of the chance resolution tree is to make sure that your product discovery efforts are transferring enterprise metrics, and that you simply’re delivery experiments relatively than options.

In every assembly, give stakeholders the house to ask questions in regards to the alternatives that you simply’ve prioritized and the answer concepts which can be on the desk. That method, they turn into an energetic participant within the product choice making course of, the place they’ll contribute their concepts for doable buyer pains to deal with or doable resolution concepts.

And, any time a stakeholder pushes for a selected function that doesn’t align with the metric or with a previously-identified alternative space, you’ll be able to gently however firmly clarify that their function is unlikely to maneuver prioritized enterprise outcomes.

When you’ve gained the belief of your stakeholders by these conferences, you’ll be able to ship updates asynchronously, to assist save on effort and time. That mentioned, make sure you set clear expectations that stakeholders can and may attain out to you if they’ve questions, considerations, strategies, or new concepts so as to add to the OST.

Transferring to a brand new OST

After every quarter, you’ll have to resolve whether or not to proceed to remain on the identical OST or pivot to a brand new one. On the finish of the day, each OST will wind up with diminishing marginal returns.For instance, conversion charges can by no means exceed 100%.

Each time we resolve that we’ll not get the very best ROI by concentrating on the preliminary metric, we have to determine a brand new metric to kick off a brand new OST. At that time, we have to restart all the OST workflow that we mentioned in “establishing your first OST.”

Nonetheless, we are able to speed up the method a bit, since stakeholders ought to now be acquainted with the foundational ideas behind OSTs. You need to solely want one to 2 conferences to align on the enterprise metric, on the chance house, and on the one alternative to give attention to.

After getting the chance space to give attention to, it’s best to have the ability to run with options and experiments with out extra enter (supplied that you simply’ve constructed up sufficient belief over time with stakeholders, and that you simply’ve been addressing their questions as they arrive up).

We now know easy methods to share OSTs with stakeholders. However how precisely can we visually monitor our progress as we run experiments? Under, we offer a template for easy methods to spin up a visible dashboard that gives related context and retains product discovery front-and-center.

Connecting OSTs to analytics

Alternative resolution bushes pair naturally effectively with analytics capabilities. In any case, we don’t know whether or not a given resolution has made significant progress for the enterprise until we analyze its efficiency, and we gained’t know whether or not we should always ship an answer or not until we are able to precisely craft an unbiased experiment.

For instance of the way you convey your alternative resolution tree to life by analytics, we’ll talk about how to take action utilizing Amplitude Notebooks.

Create an Amplitude Pocket book to your given alternative resolution tree. This pocket book will function a centralized hub for info.

The very very first thing on the prime of your pocket book needs to be the Key Consequence that you simply’re making an attempt to maneuver by your product discovery efforts. That method, we are able to see whether or not the important thing consequence has truly been enhancing over time based mostly on our experimentation efforts.

Proper after that, we then add our alternative resolution tree diagram as a picture beneath the important thing consequence dashboard. We are able to create this diagram in any type of diagramming software, e.g., Miro.

Afterwards, we should always then listing out every experiment that we’ve carried out to date. These experiments needs to be labeled with the given alternative that they’re concentrating on within the alternative resolution tree.

On the very finish of your pocket book, it’s best to embody an appendix that hyperlinks out to the entire buyer interviews and buyer analysis you carried out to flesh out the chance resolution tree. That method, you’ve proof and proof of the alternatives that you simply determined to deal with.

Closing ideas

By utilizing alternative resolution bushes, we weave in steady product discovery all through all factors of the product planning course of. And, to assist stakeholders embrace product discovery, we should always use the chance resolution tree framework.

We should always really feel empowered to tweak the method and the terminology to suit our particular contexts and desires, so long as we hold these three rules in thoughts:

1. Product discovery should serve enterprise targets.
2. The purpose of a product staff is to iterate on options that can handle buyer ache, relatively than delivery options by some arbitrary deadline.
3. We as a staff should have the ability to react to buyer learnings and suggestions in actual time, relatively than ready till a quarterly or annual cadence.

After we expose stakeholders and executives to alternative resolution bushes it should take a while for them to get used to it. However, as soon as we convey them alongside on the journey, we’ll discover that they’ll be extra aligned with our product choices. Even higher, they turn into energetic collaborators and contributors, and so they’ll share extra concepts and buyer context than you may think.

For those who loved this put up, try my others or go to Product Instructor for extra on product administration.